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Background To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. Methods One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n  = 49; luminal B, n  = 8; HER2-enriched, n  = 11; triple negative, n  = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n  = 17; luminal B, n  = 17; triple negative, n  = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k -nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. Results In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). Conclusions In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Objectives To assess radiologists’ current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template. Materials and methods An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients. Results A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists’ experience ( p < 0.001), and experience also showed low but significant correlations with communication with clinicians ( r = − 0.27, p = 0.003), report quality ( r = 0.19, p = 0.043), and error rate ( r = − 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality ( p = 0.036). Conclusion Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians. Clinical relevance statement Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement. Key Points • The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.

Background Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI. Methods In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI ( n  = 24: 12 benign and 12 malignant) and poor DWI quality ( n  = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10 −3  mm 2 /s) or malignant (≤ 1.3 × 10 −3  mm 2 /s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured. Results There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = − 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy. Conclusions Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

PurposeTo compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping.ProceduresIn this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard.ResultsFor lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM).ConclusionsRadiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

The role of extranodal (EN) sites as potential sanctuary regions resistant to CD19-directed chimeric antigen receptor T-cell (CAR-T) therapy in large B-cell lymphoma (LBCL) remains unclear. To investigate this, we retrospectively analyzed 283 adults treated with commercial CD19 CAR-T therapy, assessing 958 PET-CT scans across four time points: pre-apheresis, pre-lymphodepletion, best response, and relapse. EN involvement prior to CAR-T therapy was common (76%). Outcomes for patients with exclusive EN disease were similar to those with nodal (ND) disease alone; however, patients with concomitant EN and ND disease (EN + ND) had lower complete response rates and shorter progression-free survival. Site-specific outcomes varied: lungs/pleura/pericardium and gastrointestinal/peritoneum involvement had the lowest local response rates (48% and 51%, respectively). Notably, the risk of same-site relapse was highest in the lungs/pleura/pericardium (hazard ratio [HR] 7.8) and gastrointestinal/peritoneum (HR 5.97). Among patients relapsing after CAR-T, two-year overall survival rates from time of relapse were significantly lower in those with EN relapse (23% for exclusive EN; 25% for EN + ND) compared to exclusive ND relapse (64%; p  = 0.008). These findings underscore the high prevalence of EN disease in CAR-T recipients and its site-specific impact on outcomes, highlighting the need for organ-targeted strategies to enhance treatment efficacy. Differential site-specific response and relapse/progression risk according to pre-CAR-T therapy anatomical site involvement in Large B-cell Lymphoma. Risk of site-specific relapse or progression was not evaluable for CNS/orbital/cranial sinuses, adrenal/genitourinary, hepatobiliary/pancreas, and spleen due to insufficient number of events.

Background Evaluation of latest generation automated attenuation-based tube potential selection (ATPS) impact on image quality and radiation dose in contrast-enhanced chest-abdomen-pelvis computed tomography examinations for gynaecologic cancer staging. Methods This IRB approved single-centre, observer-blinded retrospective study with a waiver for informed consent included a total of 100 patients with contrast-enhanced chest-abdomen-pelvis CT for gynaecologic cancer staging. All patients were examined with activated ATPS for adaption of tube voltage to body habitus. 50 patients were scanned on a third-generation dual-source CT (DSCT), and another 50 patients on a second-generation DSCT. Predefined image quality setting remained stable between both groups at 120 kV and a current of 210 Reference mAs. Subjective image quality assessment was performed by two blinded readers independently. Attenuation and image noise were measured in several anatomic structures. Signal-to-noise ratio (SNR) was calculated. For the evaluation of radiation exposure, CT dose index (CTDI vol ) values were compared. Results Diagnostic image quality was obtained in all patients. The median CTDI vol (6.1 mGy, range 3.9–22 mGy) was 40 % lower when using the algorithm compared with the previous ATCM protocol (median 10.2 mGy · cm, range 5.8–22.8 mGy). A reduction in potential to 90 kV occurred in 19 cases, a reduction to 100 kV in 23 patients and a reduction to 110 kV in 3 patients of our experimental cohort. These patients received significantly lower radiation exposure compared to the former used protocol. Conclusion Latest generation automated ATPS on third-generation DSCT provides good diagnostic image quality in chest-abdomen-pelvis CT while average radiation dose is reduced by 40 % compared to former ATPS protocol on second-generation DSCT.

PurposeTo investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade.ProceduresThis retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference.ResultsRadiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI).ConclusionsRadiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

Disease progression is a substantial challenge in patients with non-Hodgkin lymphoma (NHL) undergoing chimeric antigen receptor T cell (CAR-T) therapy. Here we present InflaMix (INFLAmmation MIXture Model), an unsupervised quantitative model integrating 14 pre-CAR-T infusion laboratory and cytokine measures capturing inflammation and end-organ function. Developed using a cohort of 149 patients with NHL, InflaMix revealed an inflammatory signature associated with a high risk of CAR-T treatment failure, including increased hazard of death or relapse (hazard ratio, 2.98; 95% confidence interval, 1.60–4.91; P  < 0.001). Three independent cohorts comprising 688 patients with NHL from diverse treatment centers were used to validate our approach. InflaMix consistently and reproducibly identified patients with a higher likelihood of disease relapse and mortality, and it provided supplementary predictive value beyond established prognostic markers, including tumor burden. Moreover, InflaMix exhibited robust performance in cases with missing data, maintaining accuracy when considering only six readily available laboratory measures. These findings show that InflaMix is a valuable tool for point-of-care clinical decision-making in patients with NHL undergoing CAR-T therapy. A preinfusion circulatory inflammation biomarker-based signature predicts the likelihood of treatment failure in patients with non-Hodgkin lymphoma who were treated with CAR-T cell therapy, with an inflammatory cluster assignment being prognostic of clinical response and survival outcomes.